Is AI Your New Shopping Buddy?

I recall developing store-level sales forecasts based on past sales data and economic conditions. Building out these models took significant time and resources and could take up to a week to update with new data. All of these efforts could be rendered meaningless as a result of unpredictable events such as weather.

Related Article: Microsoft’s Raj Krishnan on AI-Driven Customer Support

Smart Tech for Smarter Shopping Recommendations

Through machine learning (ML) and AI customer service, an employee could have an application on their phone that already reads information such as time and day, then by locating a specific shelf area that the customer is browsing, make recommendations for items, alternatives and accessories. All the varied scenarios — such as outdoor weather conditions — can help understand the scenario.  

Current tools could be set up to have a store employee getting key information on the best way to approach a customer based on time of day and day of the week (data already contained in the model) along with simple details about the customer (which section of the store they are browsing). You already have an idea of what they are looking for, so the predictive model should be able to tell you what could be the best option (for both the store and the customer) and recommended accessories.

AI Timing: Perfecting the Art of Customer Approach

We can use machine learning and AI customer service models to look at all the varied scenarios and predict the best moment to approach a customer. This AI customer service model could be based on where products are on a shelf in a store, understanding the time of day or other variables. Will it work perfectly for everyone? Probably not. However, it does give employees the opportunity to optimize both the customer experience and potential basket size. 

Using AI to Support the Customer Experience

There are many other scenarios that can apply machine learning to support the customer experience. It may not yet be the best option for providing customer service through chatbots on a 24-7 basis (yet), but imagine the possibilities AI customer service can provide right now. 

Improved Workflows

AI customer service can help improve customer facing workflows. When a customer has a question, the support employee can immediately access a treasure trove of information for a response and validate that output before providing customers with a response.

Improved Omnichannel Experience

AI can enhance cross-channel service. When a customer reaches out through one channel, immediate updates to the knowledge base assures consistency across all channels where the customer might interact with the brand.

Better Proactive Offers

Machine learning models can support proactive offerings. Printers already email us when our ink is getting low, but ML could also understand through purchase decisions such as when it might be time to buy more eggs or replenish toilet tissue.

Parting Thoughts

Even in the customer experience research space, artificial intelligence can help us to speed insights to the front line. There are tools that can help take a customer interview and build a tailored touchpoint customer journey map. Customer experience response data can be analyzed across hundreds of data points to provide intuitive actions to the teams consuming the data. Even responses to closed-loop feedback can be better expedited through the use of AI customer service in building responses. 

For years, organizations have attempted to move from reactive to proactive; with artificial intelligence, there are greater possibilities in transforming those interactions. In time, the machine models will be able to help organizations make decisions easier and quicker for customers.

And maybe then the store employee will find the sweet spot for when to approach me when I enter the store. 

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